ZOTAC GeForce RTX 2080 Ti Twin Fan Deep Learning Benchmarks
As we continue to innovate on our review format, we are now adding deep learning benchmarks. In future reviews, we will add more results to this data set.
ResNet-50 Inferencing Using Tensor Cores
ImageNet is an image classification database launched in 2007 designed for use in visual object recognition research. Organized by the WordNet hierarchy, hundreds of image examples represent each node (or category of specific nouns).
In our benchmarks for Inferencing, a ResNet50 Model trained in Caffe will be run using the command line as follows.
nvidia-docker run --shm-size=1g --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --rm -v ~/Downloads/models/:/models -w /opt/tensorrt/bin nvcr.io/nvidia/tensorrt:18.11-py3 giexec --deploy=/models/ResNet-50-deploy.prototxt --model=/models/ResNet-50-model.caffemodel --output=prob --batch=16 --iterations=500 --fp16
–deploy: Path to the Caffe deploy (.prototxt) file used for training the model
–model: Path to the model (.caffemodel)
–output: Output blob name
–batch: Batch size to use for inferencing
–iterations: The number of iterations to run
–int8: Use INT8 precision
–fp16: Use FP16 precision (for Volta or Turing GPUs), no specification will equal FP32
We can change the batch size to 16, 32, 64, 128 and precision to INT8, FP16, and FP32.
The results are in inference latency (in seconds.) If we take the batch size / Latency, that will equal the Throughput (images/sec) which we plot on our charts.
We also found that this benchmark does not use two GPU’s; it only runs on a single GPU. You can, however, run different instances on each GPU using commands like.
```NV_GPUS=0 nvidia-docker run ... &
NV_GPUS=1 nvidia-docker run ... &```
With these commands, a user can scale workloads across many GPU’s. Our graphs show combined totals.
We start with Turing’s new INT8 mode which is one of the benefits of using the NVIDIA RTX cards.
Using precision of INT8 is by far the fastest inferencing method if at all possible converting code to INT8 will yield faster runs.
We see the NVIDIA GeForce RTX 2060 Super runs relatively close to the ZOTAC RTX 2070 Blower 8G.
Let us next look at FP16 and FP32 results.
Here we see that the newer cards with more compute power perform well. For $50 we think if you need AI inferencing performance, the new Super model is a worthwhile upgrade over the GeForce RTX 2060 6G.
ResNet-50 Training using Tensor Cores and Tesnorflow
We also wanted to train the venerable ResNet-50 using Tensorflow. During training the neural network is learning features of images, (e.g. objects, animals, etc.) and determining what features are important. Periodically (every 1000 iterations), the neural network will test itself against the test set to determine training loss, which affects the accuracy of training the network. Accuracy can be increased through repetition (or running a higher number of epochs.)
The command line we will use is:
nvidia-docker run --shm-size=1g --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -v ~/Downloads/imagenet12tf:/imagenet --rm -w /workspace/nvidia-examples/cnn/ nvcr.io/nvidia/tensorflow:18.11-py3 python resnet.py --data_dir=/imagenet --layers=50 --batch_size=128 --iter_unit=batch --num_iter=500 --display_every=20 --precision=fp16
Parameters for resnet.py:
–layers: The number of neural network layers to use, i.e. 50.
–batch_size or -b: The number of ImageNet sample images to use for training the network per iteration. Increasing the batch size will typically increase training performance.
–iter_unit or -u: Specify whether to run batches or epochs.
–num_iter or -i: The number of batches or iterations to run, i.e. 500.
–display_every: How frequently training performance will be displayed, i.e. every 20 batches.
–precision: Specify FP32 or FP16 precision, which also enables TensorCore math for Volta and Turing GPUs.
While this script TensorFlow cannot specify individual GPUs to use, they can be specified by
setting export CUDA_VISIBLE_DEVICES= separated by commas (i.e. 0,1,2,3) within the Docker container workspace.
We will run batch sizes of 16, 32, 64, 128 and change from FP16 to FP32. Our graphs show combined totals.
Some GPU’s like the new Super cards as well as the GeForce RTX 2060, RTX 2070, RTX 2080 and RTX 2080 Ti will not show higher batch size runs because of limited memory.
There are two very important items to note in our Tensorflow results here. First, we see results very close to the outgoing NVIDIA GeForce RTX 2070 8GB non-Super. Second, we are able to use a batch size of 64 with FP32 on the NVIDIA GeForce RTX 2060 Super because of the 8GB of memory. In contrast, the NVIDIA GeForce RTX 2060 6GB card could only use batch sizes of 16 and 32. That yields an immediate performance increase compounded by the additional compute power. If you are getting your feet wet in deep learning training, we highly suggest the new NVIDIA GeForce RTX 2060 Super 8GB for $50 more.
Next, we are going to look at the NVIDIA GeForce RTX 2060 Super power and temperature tests and then give our final words.